A simple script to implement linear algebra functions not provided by the Lua standard API, developed especially for use on Roblox
Wally users can install this package by adding the following line to their Wally.toml under [dependencies]:
linalg = "bytebit/[email protected]"
Then just run wally install.
Model files are uploaded to every release as .rbxmx files. You can download the file from the Releases page and load it into your project however you see fit.
New versions of the asset are uploaded with every release. The asset can be added to your Roblox Inventory and then inserted into your Place via Toolbox by getting it here.
linalg.matrix.new = function(rows)
Creates a new matrix
Parameters:
rows(array<array<number>>)
A (m x n) array of numbers to fill the matrix with
Returns:
[t:(m x n) matrix] The new matrix
linalg.matrix.identity = function(n)
Creates an identity matrix of size (n x n)
Parameters:
n(number)
The size of the matrix
Returns:
[t:(n x n) matrix] The identity matrix
linalg.matrix.zeros = function(m, n)
Creates a matrix of all zeros of size (m x n)
Parameters:
m(number)n(number)
Returns:
[t:(m x n) matrix] The zeros matrix
linalg.matrix.zerosLike = function(mat)
Creates a matrix of all zeros of the same size as the provided matrix
Parameters:
[t:(m
x n) matrix] mat The matrix to copy the size of
Returns:
[t:(m x n) matrix] The zeros matrix
linalg.matrix.diagonal = function(values)
Creates a diagonal matrix with the values provided as the diagonal entries
Parameters:
values(array<number>)
An n-length array whose entries will be set as the diagonal entries
Returns:
[t:(n x n) matrix] The resulting diagonal matrix
linalg.matrix.isDiagonal = function(mat)
Determines whether a matrix is diagonal Does not exclusively refer to square matrices Refers strictly to whether all non-zero values are on the main diagonal (i.e., a_{ij} = 0 for all i, j where i ~= j)
Parameters:
[t:(m
x n) matrix] mat The matrix to check
Returns:
boolean
True if the matrix is diagonal, false otherwise
linalg.matrix.isUpperTriangular = function(mat)
Determines whether a matrix is upper triangular Note that any non-square matrix will return false
Parameters:
[t:(m
x n) matrix] mat The matrix to check
Returns:
boolean
True if the matrix is upper triangular, false otherwise
linalg.matrix.isLowerTriangular = function(mat)
Determines whether a matrix is lower triangular Note that any non-square matrix will return false
Parameters:
[t:(m
x n) matrix] mat The matrix to check
Returns:
boolean
True if the matrix is lower triangular, false otherwise
linalg.matrix.transpose = function(mat)
Creates a new matrix that is the transpose of the provided matrix
Parameters:
[t:
(m x n) matrix] mat The matrix to create the transpose of
Returns:
[t: (n x m) matrix] The transpose of mat
linalg.matrix.expm = function(mat, numIterations)
Solves for e^mat Defined as: e^A = \sum_{k=0}^{\infinity} \frac{1}{k!} A^k Implemented in a naive way to approximate by using iterations Runtime of O(n^3)
Parameters:
[t:(n
x n) matrix] mat The matrix to use as the exponentnumIterations(number)
The number of iterations to take the sum of the taylor series to
Returns:
The matrix exponential approximation
linalg.vector.new = function(values)
Creates a new column vector
Parameters:
values(array<number>)
The values to have for the column vector
Returns:
[t:(n x 1) matrix] The new column vector
linalg.vector.e = function(i, n)
Creates the standard basis vector i for R^n That is, creates a vector of length n with all zeros except at index i which will have value 1
Parameters:
i(number)
The index of en(number)
The dimensionality of the vector
Returns:
[t:(n x 1) matrix] The standard basis vector e_i in R^n
linalg.vector.norm.l1 = function(v)
The L1 norm of a vector sum_i{|v_i|}
Parameters:
[t:(n
x 1) matrix] v The vector
Returns:
number
The resulting value
linalg.vector.norm.l2 = function(v)
The L2 norm of a vector sqrt(sum_i{(v_i)^2})
Parameters:
[t:(n
x 1) matrix] v The vector
Returns:
number
The resulting value
linalg.vector.norm.linf = function(v)
The L-infinity norm of a vector max{v}
Parameters:
[t:(n
x 1) matrix] v The vector
Returns:
number
The resulting value
linalg.vector.ip.dot = function(v1, v2)
Computes the standard dot product of two vectors Defined as \sum_{i=0}^{n-1} v1[i] * v2[i]
Parameters:
[t:(n
x 1) matrix] v1 The first vector[t:(n
x 1) matrix] v2 The second vector
Returns:
number
The result
linalg.vector.project = function (v, u, innerProductFunc, normFunc)
Projects vector v onto vector space u Defined as \sum_{i=0}^{m-1} <v, u[i]>/|u[i]|^2 * u[i]
Parameters:
[t:(n
x 1) matrix] v The vector to project onto u[t:array<(n
x 1) matrix>] u The vector space to project v onto (can also be just one vector)[t:function([(n
x 1) matrix], [(n x 1) matrix])?] innerProductFunc The inner product function to use; Defaults to the dot product[t:function([(n
x 1) matrix])?] normFunc The norm function to use; Defaults to the L2 norm
Returns:
The vector projection of v onto u
linalg.vector.unit = function(v, normFunc)
Gets the unit vector with the same direction as the provided vectr
Parameters:
[t:(n
x 1) matrix] v The vector with the appropriate directionnormFunc(function?)
The function to use as the norm; Defaults to the L2 norm
Returns:
[t:(n x 1) matrix] The unit vector
linalg.vector.createArbitraryAxisRotationMatrix = function(v, theta)
Creates a matrix that rotates a vector about an arbitrary vector Only works for 3 dimensions
Parameters:
[t:(n
x 1) matrix] v The vector to rotate about (should be a unit vector)theta(number)
The angle to rotate by (in radians)
Returns:
[t:nxn matrix] The resulting linear operator
linalg.gramSchmidt = function (u, epsilon, dim, innerProductFunc, normFunc)
Creates an orthonormal basis for a dim-dimensional inner product space
Parameters:
[t:array<(n
x 1) matrix>] u The list of matrices to add to the basis (will be converted to unit vectors) (can be a single vector instead of an array)epsilon(number?)
The minimum norm value for a vector to count to be added to the basis; Defaults to 0.01[t:function([(n
x 1) matrix], [(n x 1) matrix])?] innerProductFunc The inner product function to use; Defaults to the dot product[t:function([(n
x 1) matrix])?] normFunc The norm function to use; Defaults to the L2 norm
Returns:
[t:array<(n x 1) matrix>] An orthonormal basis that includes the unit vectors of the original u